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Jdemetra+ User Guide Version JDemetra+ User Guide Version 2.2 Sylwia Grudkowska Department of Statistics Warsaw, 2017 r. Sylwia Grudkowska – Narodowy Bank Polski, Department of Statistics [email protected], (+48) 22 585 92 48 The views expressed herein are those of the authors and not necessarily those of the Narodowy Bank Polski Print: NBP Printshop Published by: Narodowy Bank Polski Education & Publishing Department ul. Świętokrzyska 11/21 00-919 Warszawa, Poland phone +48 22 653 23 35 www.nbp.pl © Copyright Narodowy Bank Polski, 2017 Table of content 1. Introduction 5 1.1. Historical background 5 1.2. About JDemetra+ 7 1.3. About JDemetra+ User Guide 9 1.3.1. Who should use this document? 9 1.3.2. How the document is organized 9 1.3.3. How to use this document 10 2. Preliminary issues: uploading and visualizing data 12 2.1.1. Overview of the JDemetra+ 12 2.1.2. Source data 14 2.1.3. Import data 15 2.1.4. Displaying data 18 3. Seasonal adjustment and other time-series analysis with JDemetra+ 22 3.1. Simple seasonal adjustment 23 3.1.1. Simple seasonal adjustment of single time series 23 3.1.2. Simple seasonal adjustment of multiple time series 32 3.2. Detailed seasonal adjustment 42 3.2.1. Detailed seasonal adjustment of single time series 42 3.2.2. Detailed seasonal adjustment of multiple time series 78 3.3. Time series modelling 100 3.3.1. Basic time series analysis 100 3.3.2. Advanced time series analysis 103 3.4. Other tools 112 3.4.1. Seasonality tests 112 3.4.2. Spectral graphs 121 3.4.3. Calendars 127 4. References 151 Acknowledgements: I am deeply grateful to Veronique Elter (STATEC), Duncan Elliott and James Macey (Office for National Statistics), Jean Palate and David de Antonio Liedo (The National Bank of Belgium), Dominique Ladiray and Trong-Hien Pham (INSEE), Christiane Hofer, Andreas Dietrich and Andreas Lorenz (Deutsche Bundesbank) for their valuable support in the preparation of this document. Thanks are to Faiz Alsuhail (Statistics Finland), Karen Keller (Statistics Denmark), Regina Soares (Statistics Portugal) and Yingfu Xie (Statistics Sweden) for their insightful comments and suggestions. Finally, I would like to thank the all the members of the Seasonal Adjustment Centre of Excellence for their useful comments and helpful suggestions on various drafts of this document. Disclaimer: The JDemetra+ User Guide is provided by Eurostat. This material: ▪ is information to assist new users of JDemetra+ to familiarize themselves with the interface and functionalities of the application in a general nature and is not intended to favour one method over another out of those available in the application; ▪ is still in development; ▪ sometimes links to further papers and documents for which Eurostat has no control and for which Eurostat assumes no responsibility; ▪ does not constitute professional or legal advice. JDemetra+ is designed to support the ESS Guidelines on Seasonal Adjustment (2015). While JDemetra+ incorporates the seasonal adjustment methods of the U.S. Census Bureau (X-12-ARIMA and X- 13ARIMA-SEATS) and of the Bank of Spain (TRAMO/SEATS), the ESS Guidelines on Seasonal Adjustment (2015) do not promote one method over another. The paper presents the personal opinions of the author and does not necessarily reflect the official position of the institutions with whom the author cooperates. All errors are author’s responsibility. Contact: Sylwia Grudkowska – Narodowy Bank Polski, Department of Statistics [email protected], (+48) 22 585 92 48 4 1. Introduction 1.1. Historical background Seasonal adjustment (SA) is an important component of the official statistics business process. This technique is widely used for estimating and removing seasonal and calendar-related movements from time series resulting in data that present a clear picture of economic phenomena. For these reasons Eurostat1 takes part in various activities that aim to promote, develop and maintain a publicly available software solution for SA in line with established best practice. Among many seasonal adjustment methods that produce reliable results for large datasets the most widely used and recommended are X-12-ARIMA2 (X-13ARIMA-SEATS3) developed at the U.S. Census Bureau and TRAMO/SEATS4 developed by Victor Gómez and Agustín Maravall, from the Bank of Spain. Both methods are divided into two main parts. The first part is called pre-adjustment and removes deterministic effects from the series by means of a regression model with ARIMA noise. The second part is the decomposition of the time series to estimate and remove a seasonal component. TRAMO/SEATS and X-12-ARIMA use a very similar approach in the first part to estimate the same model on the processing step, but they differ completely in the decomposition step. Therefore, comparing results from decomposition is often difficult. Furthermore, their diagnostics focus on different aspects and their outputs take completely different forms. Both the above seasonal adjustment programs were originally written in FORTRAN, which is currently recognized as a declining language. The FORTRAN limitations - especially for the 1 Eurostat is the statistical office of the European Union. Its task is to provide the European Union with statistics at European level that enable comparisons between countries and regions. More information at http://epp.eurostat.ec.europa.eu/portal/page/portal/eurostat/home/. 2 X-12-ARIMA is a seasonal adjustment program developed and supported by the U.S. Census Bureau. It includes all the capabilities of the X-11 program (see Dagum, E.B.D. (1980)) which estimates trend and seasonal component using moving averages. X-12-ARIMA offers useful enhancements including: extension of the time series with forecasts and backcasts from ARIMA models prior to seasonal adjustment, adjustment for effects estimated with user-defined regressors, additional seasonal and trend filter options, alternative seasonal-trend-irregular decomposition, additional diagnostics of the quality and stability of the adjustments, extensive time series modelling and model selection capabilities for linear regression models with ARIMA errors. For basic information on the X-12-ARIMA program see X-12-ARIMA Reference Manual (2011). More information on X- 12-ARIMA can be found at http://www.census.gov. 3 X-13ARIMA-SEATS is a seasonal adjustment program developed and supported by the U.S. Census Bureau that contains two seasonal adjustment modules: the enhanced X-11 seasonal adjustment procedure and ARIMA model based seasonal adjustment procedure from the SEATS seasonal adjustment program developed by Gomez, V. and Maravall, A. (2013). For information on the X-3ARIMA-SEATS program see X-13ARIMA-SEATS Reference Manual (2013). More information on X13ARIMA-SEATS can be found at http://www.census.gov. 4 TRAMO/SEATS is a model-based seasonal adjustment method developed by Victor Gomez and Agustin Maravall (the Bank of Spain). It consists of two linked programs: TRAMO and SEATS. TRAMO ("Time Series Regression with ARIMA Noise, Missing Observations, and Outliers") performs estimation, forecasting, and interpolation of regression models with missing observations and ARIMA errors, in the presence of possibly several types of outliers. SEATS ("Signal Extraction in ARIMA Time Series") performs an ARIMA-based decomposition of an observed time series into unobserved components. Both programs are supported by the Bank of Spain. For basic information on the TRAMO/SEATS see Caporello, G., and Maravall, A. (2004). More information on TRAMO/SEATS can be found at www.bde.es. 5 creation of reusable components and for the management of complex problems - make the maintenance of the relevant IT codes increasingly burdensome. These original seasonal adjustment programs are commonly perceived by users as difficult to operate. Therefore, to improve access to SA methods for non-specialists, Eurostat introduced new software called Demetra. It offered a user-friendly interface to the two SA algorithms: TRAMO/SEATS and X-12-ARIMA and facilitated the comparison of the output from those two algorithms. Even so, Demetra uses the FORTRAN libraries, which, together with insufficient product development and handling of errors, is a factor that caused a rapid decline in software’s usage. In 2009, the European Statistical System (ESS) launched its Guidelines on Seasonal Adjustment5. As Demetra could not be adapted to the new requirements in the Guidelines, Eurostat, in cooperation with the National Bank of Belgium (NBB), started a project aiming to develop improved software called Demetra+6. It was released in 2012. This tool provides a common approach for seasonal adjustment using TRAMO/SEATS and X-12-ARIMA methods, which is more coherent with the Guidelines. It includes a unified graphical interface and input/output diagnostics for the two methods. Demetra+ source code is written in C++ and uses the two original FORTRAN modules, as well as .NET libraries. Therefore Demetra+ software is non-extensible and cannot be used in IT environments other than Windows. For these reasons it seems that in long-term perspective it will not meet users’ expectations. Therefore, Eurostat took an initiative to create new software that is based on Demetra+ experience but is platform independent and extensible. The resulting program is called JDemetra+ and was developed by the NBB in 2012-2014. From the typical user perspective in comparison with Demetra+, numerous improvements have been implemented in JDemetra+, in terms of both layout and functionalities. The most critical innovation is the re-writing of the original FORTRAN codes of X-12-ARIMA and TRAMO/SEATS in JAVA, following a real object-oriented approach. These functionalities are discussed in the next section. 5 Endorsed by the Statistical Programme Committee, the European Statistical System (ESS) Guidelines on Seasonal Adjustment (2009) aim to harmonize European practices and to improve the comparability of infra-annual national statistics as well as enhance the overall quality of the European Union and the euro area aggregates.
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